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Routine Electronic Health Record (EHR) blood pressure charting under-samples dynamic physiology, risking missed hemodynamic instability. This study quantifies how HER-like down-sampling changes the detection and burden of hypo- and hypertension versus continuous monitoring and articulates the consequences for clinical decision support and machine learning label quality. We retrospectively analyzed 78 ECMO-supported pediatric patients (2019-2023). The continuous mean arterial pressure (MAP) captured every 5 s was resampled at intervals from 5 s to 1 h. We screened for 3 min windows of hypotension or hypertension at 10th/90th age-normed thresholds, comparing the per-patient event frequency and burden with EHR-derived recordings. At 10th/90th thresholds, hypotension events fell from 13,936 (5 s) to 3803 (15 min; -72.7%); the EHR captured 3471. Hypertension events dropped from 1573 to 410 (-73.9%); the EHR registered 1587. The EHR data overstated hypertension burden, indicating preferential documentation during prolonged instability while missing brief excursions. Standard EHR sampling significantly under-reports blood pressure derangements in pediatric ECMO. This underreporting of brief events may limit the accuracy of clinical decision support tools and machine learning algorithms in high-acuity patients. High-frequency data acquisition improves event capture and should be prioritized for clinical decision support and machine learning development.

High ambient temperature poses a significant public health challenge, particularly for low-income older adults (LOAs) with preexisting health and social issues and disproportionate living conditions, placing them at a vulnerable condition of heat-related illnesses and associated public health risks. This study aims to utilize advanced statistical regression and machine learning methods to analyze complex relationships between elevated temperature, physical activity (PA), sociodemographic factors and fall incidents among LOAs. We collected data from a cohort of 304 LOAs aged 60 and above, living in free-living conditions in low-income communities in Central Florida, USA. Zero-inflated Poisson regression was employed to examine the linear relationships, which reflect the zero-abundant nature of fall incidents. Then, an advanced machine learning approach-the mixed undirected graphical model (MUGM)-was employed to further explore the intricate, nonlinear relationships among daily PA, daily temperature, and fall incidents. The findings suggest that more moderate-to-vigorous PA is significantly associated with fewer fall incidents (RR = 0.90, 95% CI: (0.816, 0.993), p = 0.037), after adjusting for other variables. In contrast, elevated temperature is strongly linked to a greater risk of falls (RR = 1.733, 95% CI: (1.581, 1.901), p < 0.0001), potentially reflecting seasonal influences. Although higher temperature increases fall events, this effect is mitigated among LOAs with increased sedentary behavior (p < 0.0001). Additionally, findings from the MUGM reinforce the intricate nature of falls. Fall counts were highly correlated with race and positively associated with temperature, highlighting the importance of tailoring fall prevention strategies to account for seasonal variations and health disparities, and promoting PA.

Biofilms are resistant microbial cell aggregates that pose risks to the health and food industries and produce environmental contamination. The accurate and efficient detection and prevention of biofilms are challenging and demand interdisciplinary approaches. This multidisciplinary research reports the application of a deep learning-based artificial intelligence (AI) model for detecting biofilms produced by Pseudomonas aeruginosa with high accuracy. Aptamer DNA-templated silver nanocluster (Ag-NC) was used to prevent biofilm formation, which produced images of the planktonic states of the bacteria. Large-volume bright-field images of bacterial biofilms were used to design the AI model. In particular, we used U-Net with ResNet encoder enhancement to segment biofilm images for AI analysis. Different degrees of biofilm structures can be efficiently detected using ResNet18 and ResNet34 backbones. The potential applications of this technique are also discussed.

Today's advancements in neuroimaging have been pivotal in enhancing our understanding of brain development and function using various MRI techniques. This study utilizes images from T1-weighted imaging and diffusion-weighted imaging to identify gray matter and white matter coherent growth patterns within 2 years from 9-10-year-old participants in the Adolescent Brain Cognitive Development (ABCD) Study. The motivation behind this investigation lies in the need to comprehend the intricate processes of brain development during adolescence, a critical period characterized by significant cognitive maturation and behavioral change. While traditional methods like canonical correlation analysis (CCA) capture the linear interactions of brain regions, a deep canonical correlation analysis with an autoencoder (DCCAE) nonlinearly extracts brain patterns. The study involves a comparative analysis of changes in gray and white matter over two years, exploring their interrelation based on correlation scores, extracting significant features using both CCA and DCCAE methodologies, and finding an association between the extracted features with cognition and the Child Behavior Checklist. The results show that both CCA and DCCAE components identified similar brain regions associated with cognition and behavior, indicating that brain growth patterns over this two-year period are linear. The variance explained by CCA and DCCAE components for cognition and behavior suggests that brain growth patterns better account for cognitive maturation compared to behavioral changes. This research advances our understanding of neuroimaging analysis and provides valuable insights into the nuanced dynamics of brain development during adolescence.

Background: Electronic health records (EHR) are now widely available in healthcare institutions to document the medical history of patients as they interact with healthcare services. In particular, routine care EHR data are collected for a large number of patients. These data span multiple heterogeneous elements (i.e., demographics, diagnosis, medications, clinical notes, vital signs, and laboratory results) which contain semantic, concept, and temporal information. Recent advances in generative learning techniques were able to leverage the fusion of multiple routine care EHR data elements to enhance clinical decision support.

Objective: A scoping review of the proposed techniques including fusion architectures, input data elements, and application areas is needed to synthesize variances and identify research gaps that can promote re-use of these techniques for new clinical outcomes.

Design: A comprehensive literature search was conducted using Google Scholar to identify high impact fusion architectures over multi-modal routine care EHR data during the period 2018 to 2023. The guidelines from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping review were followed. The findings were derived from the selected studies using a thematic and comparative analysis.

Results: The scoping review revealed the lack of standard definition for EHR data elements as they are transformed into input modalities. These definitions ignore one or more key characteristics of the data including source, encoding scheme, and concept level. Moreover, in order to adapt to emergent generative learning techniques, the classification of fusion architectures should distinguish fusion from learning and take into consideration that learning can concurrently happen in all three layers of new fusion architectures (i.e., encoding, representation, and decision). These aspects constitute the first step towards a streamlined approach to the design of multi-modal fusion architectures for routine care EHR data. In addition, current pretrained encoding models are inconsistent in their handling of temporal and semantic information thereby hindering their re-use for different applications and clinical settings.

Conclusions: Current routine care EHR fusion architectures mostly follow a design-by-example methodology. Guidelines are needed for the design of efficient multi-modal models for a broad range of healthcare applications. In addition to promoting re-use, these guidelines need to outline best practices for combining multiple modalities while leveraging transfer learning and co-learning as well as semantic and temporal encoding.